This website requires JavaScript.

RefinedFields: Radiance Fields Refinement for Unconstrained Scenes

Karim KassabAntoine SchnepfJean-Yves FranceschiLaurent CaraffaJeremie MaryVal\'erie Gouet-Brunet
Feb 2024
0被引用
0笔记
摘要原文
Modeling large scenes from unconstrained images has proven to be a major challenge in computer vision. Existing methods tackling in-the-wild scene modeling operate in closed-world settings, where no conditioning on priors acquired from real-world images is present. We propose RefinedFields, which is, to the best of our knowledge, the first method leveraging pre-trained models to improve in-the-wild scene modeling. We employ pre-trained networks to refine K-Planes representations via optimization guidance using an alternating training procedure. We carry out extensive experiments and verify the merit of our method on synthetic data and real tourism photo collections. RefinedFields enhances rendered scenes with richer details and outperforms previous work on the task of novel view synthesis in the wild. Our project page can be found at https://refinedfields.github.io .
展开全部
机器翻译
AI理解论文&经典十问
图表提取
参考文献
发布时间 · 被引用数 · 默认排序
被引用
发布时间 · 被引用数 · 默认排序
社区问答